What is most remarkable about AlphaGo’s victory is that AlphaGo was not “taught” how to play Go. Instead, its multilayer neural network learned how to play, and then how to win, by playing millions of games and observing the winning strategies.

These stunning and rapid advances in software that does what humans do, but better, invite not only an optimistic question — what next? — but also a worried warning. In an editorial accompanying publication of the AlphaGo research, the journal Nature wrote:

As the use of deep neural network systems spreads into everyday life — they are already used to analyze and recommend financial transactions — it raises an interesting concept for humans and their relationships with machines. The machine becomes an oracle; its pronouncements have to be believed.

When a conventional computer tells an engineer to place a rivet or a weld in a specific place on an aircraft wing, the engineer — if he or she wishes — can lift the machine’s lid and examine the assumptions and calculations inside. That is why the rest of us are happy to fly. Intuitive machines will need more than trust: they will demand faith.

So, what does this mean for law?

The other day, a search for “artificial intelligence in law” produced 86,400 results from just the News section of Google’s vast index. From the Web as a whole, 32.8 million results and from Videos — 261,000, beginning with Jude Law’s role as Gigolo Joe in the movie A.I. (thank you, RankBrain).

Yes, there’s something going on here. But we need to parse the pile a bit. What is Artificial Intelligence (AI)? What is AI doing in law? Who is doing it? And where is it headed?

What is this thing called AI?

AI is a big forest of academic and commercial work around “the science and engineering of making intelligent machines,” in the words of the person who coined the term artificial intelligence, John McCarthy. A thorough and hype-free review of AI in business was published recently by Deloitte, Demystifying Artificial Intelligence, suggesting the term “cognitive technologies” to encourage focus on the specific, useful technologies that emerge from the broad field of AI.

However labeled, the field has many branches, with many significant connections and commonalities among them. The most active today are shown here:

Lawyers do not need robots or machine vision, but other branches of AI are indeed useful. Practical use of cognitive technologies in legal services is by no means new, nor did it begin when IBM’s general counsel predicted that Watson could pass the bar exam by 2016.

Artificial intelligence is hard at work in the law — for example, in legal research, ediscovery, compliance, contract analysis, case prediction and document automation — though often there is no “AI Inside” label on the box.

Machine learning, expert systems and other AI techniques enable lawyers to devote more of their time to more valuable (and interesting) work. Mining documents in discovery and due diligence, answering routine questions, sifting data to predict case outcomes, drafting contracts — all are faster, better, cheaper and becoming more so with the assistance of intelligent software.

The second post in this series will take a closer look at the AI tools and companies at work in law.

Before co-founding Neota Logic, Michael was the chief knowledge officer and co-head of technology at Davis Polk & Wardwell for 20 years after practicing as a litigation and bankruptcy partner at Mayer Brown. He is also a director of Pro Bono Net, which provides innovative technology for the nonprofit legal sector.